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  • In this paper, a new evolving artificial neural network using evolutionary computation is introduced. Based on the pre-defined Beta operator sets, this model called Flexible Beta Basis Function Neural Tree (FBBFNT), can be created and learned. The structure is developed using the Extended Immune Programming (EIP). The Beta parameters and connected weights are optimized using the Hybrid Bacterial Foraging Optimization algorithm. The performance of the proposed method is evaluated for nonlinear systems and compared with those of related methods.
  • In this paper, a new evolving artificial neural network using evolutionary computation is introduced. Based on the pre-defined Beta operator sets, this model called Flexible Beta Basis Function Neural Tree (FBBFNT), can be created and learned. The structure is developed using the Extended Immune Programming (EIP). The Beta parameters and connected weights are optimized using the Hybrid Bacterial Foraging Optimization algorithm. The performance of the proposed method is evaluated for nonlinear systems and compared with those of related methods. (en)
Title
  • Evolving Flexible Beta Basis Function Neural Tree for Nonlinear Systems
  • Evolving Flexible Beta Basis Function Neural Tree for Nonlinear Systems (en)
skos:prefLabel
  • Evolving Flexible Beta Basis Function Neural Tree for Nonlinear Systems
  • Evolving Flexible Beta Basis Function Neural Tree for Nonlinear Systems (en)
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  • RIV/61989100:27740/13:86089371!RIV14-MSM-27740___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ED1.1.00/02.0070)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
  • Abraham Padath, Ajith
http://linked.open.../riv/druhVysledku
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http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 73831
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  • RIV/61989100:27740/13:86089371
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  • nonlinear systems; Hybrid Bacterial Foraging Optimization algorithm; Flexible Beta Basis Function Neural Tree; Extended Immune Programming (en)
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http://linked.open...ontrolniKodProRIV
  • [4AEA8490E699]
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  • Dallas
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  • New York
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the International Joint Conference on Neural Networks 2013
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http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Abraham Padath, Ajith
  • Alimi, A. M.
  • Bouaziz, S.
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://bibframe.org/vocab/doi
  • 10.1109/IJCNN.2013.6706992
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  • IEEE
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  • 978-1-4673-6129-3
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  • 27740
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